Consider a setting where a set \(U=\lbrace u_1, \cdots , u_n\rbrace \)  of \(n\)  users want to train a supervised model \(F_w\)  on their data.
In FL, a server coordinates with the users to train a model in a privacy-preserving way, i.e., the data of each user will not be shared with the server or other users. Several methods have been proposed for training classifiers in the federated setting [1]}. The widely-used Federated Averaging framework, also called FedAvg, is described in Algorithm (REF ) [2]}.
